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Experimental Behavior Analysis Jobs in Texas (NOW HIRING)

HEB

Data Engineering Manager

San Antonio, TX · On-site

$155K/yr

Behavioral analytics * Omnichannel personalization * Design scalable data architectures utilizing ... Enable experimentation, A/B testing, feature engineering, and measurement frameworks that improve ...

Forcepoint

AI Web Optimization Specialist

Austin, TX · On-site

$250/hr

... behavioral analytics, experimentation and personalization. This role will analyze how visitors interact with the website, identify areas of friction or opportunity and translate those findings into ...

AT and T

Director-Advanced Analytics

Dallas, TX · On-site

As the organization's leader in causal measurement and experimentation, the Director will pioneer ... behavior. Key Responsibilities: Establish AT&T's Marketing Causal Truths * Develop AT&T's causal ...

AT&T

Director-Advanced Analytics

Dallas, TX · On-site

As the organization's leader in causal measurement and experimentation, the Director will pioneer ... behavior. Key Responsibilities: Establish AT&T's Marketing Causal Truths * Develop AT&T's causal ...

Expedia

Senior Data Analyst - Layla AI

Austin, TX · On-site

$85K - $107K/yr

Run product analytics - funnels, cohorts, retention, and traveler behavior - and turn the findings into clear insights that inform prioritization. * Design and analyze experiments (A/B tests ...

Expedia, Inc.

Senior Data Analyst - Layla AI

Austin, TX · On-site

$200 - $250/hr

Run product analytics -- funnels, cohorts, retention, and traveler behavior -- and turn the findings into clear insights that inform prioritization. * Design and analyze experiments (A/B tests ...

Deloitte

Manager, Experience Researcher

Dallas, TX · On-site

Strategically design, plan, and execute user research studies and analyze behavioral metrics to ... Bachelor's degree in Cognitive Psychology, Experimental Psychology, Human-Computer Interaction ...

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All JobsExperimental Behavior Analysis JobsExperimental Behavior Analysis Jobs in Texas

Experimental Behavior Analysis information

What is experimental behavior analysis?

Experimental behavior analysis is a scientific field within behavior analysis that focuses on understanding the principles of behavior through controlled experiments, often using animals or humans in laboratory settings. Researchers in this area investigate how behavior is influenced by various environmental factors, such as reinforcement and punishment. The findings from experimental behavior analysis help inform effective interventions and treatments in applied settings, including education, therapy, and behavioral health. This discipline is foundational to the broader field of behavior analysis and is closely tied to the work of B.F. Skinner and other early behaviorists.

What are the key skills and qualifications needed to thrive as an experimental behavior analyst?

To thrive as an Experimental Behavior Analyst, you need a solid foundation in behavioral psychology, research methodology, data analysis, and typically a graduate degree in psychology or a related field. Familiarity with statistical software (such as SPSS or R), experimental design platforms, and often Board Certified Behavior Analyst (BCBA) certification are essential. Strong analytical thinking, attention to detail, and effective communication skills help in interpreting data and collaborating with research teams. These competencies are crucial for designing rigorous experiments, accurately evaluating behavioral interventions, and advancing scientific understanding in the field.

What are some common challenges faced by professionals in experimental behavior analysis, and how can they be addressed?

Professionals in Experimental Behavior Analysis often encounter challenges such as designing ethically sound experiments, managing variables that can affect behavioral outcomes, and ensuring consistent data collection. These challenges can be addressed by adhering to rigorous research protocols, collaborating closely with colleagues for peer review, and staying updated on the latest ethical guidelines and methodological advancements in the field. Effective communication and teamwork within a research group are also crucial to troubleshoot issues and maintain high-quality research standards.

What is the difference between Experimental Behavior Analysis vs Applied Behavior Analysis?

AspectExperimental Behavior AnalysisApplied Behavior Analysis
CredentialsTypically requires a master's or doctoral degree in psychology or behavior analysisRequires certification (e.g., BCBA) and similar educational background
Work EnvironmentResearch settings, laboratories, academic institutionsClinical settings, schools, homes, community programs
Industry UsagePrimarily in research and academiaPrimarily in clinical practice and intervention
FocusUnderstanding fundamental principles of behavior through experimentsApplying behavior principles to improve individual behavior

Experimental Behavior Analysis focuses on research and understanding basic behavioral principles, often in laboratory settings. Applied Behavior Analysis uses these principles to develop interventions for individuals, especially in clinical and educational environments. While both fields share foundational knowledge, their applications and settings differ significantly.

Is experimental behavior analysis a good career?

Experimental behavior analysis is a specialized field focused on studying and modifying behavior through scientific methods, often requiring knowledge of psychology, data analysis, and research techniques. It offers opportunities in research, healthcare, and education, with roles typically requiring advanced degrees and certification. The career can be rewarding for those interested in scientific inquiry and behavioral interventions.

What are popular job titles related to Experimental Behavior Analysis jobs in Texas?

For Experimental Behavior Analysis jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Experimental Behavior Analysis jobs in Texas look for?

The top searched job categories for Experimental Behavior Analysis jobs in Texas are:

What cities in Texas are hiring for Experimental Behavior Analysis jobs?

Cities in Texas with the most Experimental Behavior Analysis job openings:

Experimental Behavior Analysis jobs near you
Infographic showing various Experimental Behavior Analysis job openings in Texas as of August 2026, with employment types broken down into 86% Full Time, 6% Part Time, and 8% Contract. Highlights an 88% In-person, and 12% Remote job distribution.

Data Engineering Manager

HEB

San Antonio, TX • On-site

$155K/yr

Full-time

Re-posted 16 days ago

Job description

Responsibilities
We are seeking an experienced Data Engineering Manager to lead the design, development, and delivery of scalable data platforms and data products that power personalized customer experiences across digital retail channels. This leader will manage and develop a team of Data Engineers responsible for building reliable, secure, and high-performance data pipelines, machine learning data infrastructure, and customer data solutions that enable personalized product search, search ranking, recommendations, customer segmentation, behavioral analytics, and omnichannel personalization.
As a people leader, you will be responsible for hiring, onboarding, coaching, performance management, succession planning, and career development while fostering a culture of innovation, operational excellence, and continuous improvement. You will partner closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience, and senior technology leaders to deliver strategic initiatives that drive measurable business outcomes.
The ideal candidate combines deep expertise in modern data engineering and large-scale data platforms with proven leadership experience and a strong understanding of customer behavior data, personalization systems, recommendation engines, and cloud-based technologies.
Key Responsibilities & Essential Functions
Leadership & Team Management
  • Lead, mentor, and develop a high-performing team of Data Engineers across one or more engineering squads.
  • Foster an environment of accountability, collaboration, innovation, and customer-centric thinking.
  • Manage all people leadership responsibilities, including hiring, onboarding, performance reviews, career development, promotions, succession planning, compensation planning, and employee engagement.
  • Coach and mentor engineers in engineering best practices, technologies, processes, and career growth.
  • Empower team members to be autonomous, highly effective, and capable of delivering scalable solutions.
  • Establish engineering standards, coding practices, operational excellence frameworks, and delivery processes.
  • Drive Agile planning, sprint execution, prioritization, and delivery of strategic initiatives.

Data Platform & Engineering
  • Lead the design, development, and operation of scalable batch, streaming, and real-time data platforms.
  • Develop and maintain data products supporting:
    • Personalized product search
    • Search relevance and ranking optimization
    • Product recommendations
    • Nice to have:
    • Customer segmentation
    • Customer identity and householding
    • Behavioral analytics
    • Omnichannel personalization
  • Design scalable data architectures utilizing modern lakehouse, data lake, and cloud-native patterns.
  • Build and support feature stores, APIs, and data services used by machine learning and personalization systems.
  • Ensure high levels of data quality, reliability, observability, governance, security, and compliance.
  • Optimize platform performance, scalability, availability, and cost efficiency.
  • Implement monitoring, alerting, SLA management, and incident response procedures for production data platforms.

Technical Strategy & Architecture
  • Develop technical roadmaps aligned with business priorities and long-term organizational objectives.
  • Lead the technical design and delivery of complex initiatives across multiple systems and platforms.
  • Recommend improvements to architecture, scalability, reliability, security, performance, and operational processes.
  • Evaluate emerging technologies and industry best practices to enhance platform capabilities.
  • Guide engineering teams on architectural decisions, code quality, design reviews, and technical standards.
  • Assist in diagnosing and resolving highly complex technical and operational issues.

Customer Personalization & Machine Learning Enablement
  • Build foundational data capabilities that support:
    • Product recommendation engines
    • Purchase behavior analysis
    • Real-time personalization
    • Search relevance optimization
    • Behavioral event processing
      Nice to Have:
    • Customer 360 platforms
    • Customer identity resolution
    • Clickstream analytics
  • Partner with Data Scientists and Machine Learning Engineers to operationalize and scale personalization models.
  • Enable experimentation, A/B testing, feature engineering, and measurement frameworks that improve customer experiences.

Cross-Functional Collaboration
  • Collaborate closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience teams, and business stakeholders.
  • Translate business objectives into scalable technical solutions and execution plans.
  • Communicate technical strategy, progress, risks, recommendations, and outcomes to leaders and stakeholders.
  • Lead cross-functional initiatives with significant business impact and organizational visibility.

Operational Excellence
  • Establish operational objectives, work plans, staffing strategies, and resource allocations.
  • Ensure adherence to budgets, timelines, and performance requirements.
  • Implement strategic policies, processes, and standards that support departmental and organizational objectives.
  • Drive continuous improvement through modern engineering practices, automation, observability, and operational excellence.

Qualifications & Key Requirements
Work Experience
  • 8+ years of experience in software engineering, data engineering, or related technical disciplines.
  • 3+ years of experience leading and developing engineering teams.
  • Proven experience delivering large-scale data platform, analytics, or machine learning infrastructure initiatives.
  • Experience managing technical roadmaps, cross-functional projects, and engineering delivery.

Knowledge, Skills & Abilities
  • Strong leadership skills with demonstrated success building and managing high-performing engineering teams.
  • Expert knowledge of data architecture, distributed systems, software design patterns, and engineering best practices.
  • Deep understanding of data modeling, ETL/ELT, streaming architectures, and event-driven systems.
  • Strong expertise with:
    • Python
    • SQL
    • Apache Spark
    • Kafka
    • Data orchestration frameworks
  • Experience with cloud platforms such as AWS and/or Google Cloud Platform.
  • Experience with modern data lake and lakehouse architectures.
  • Experience building APIs, data products, and services supporting machine learning applications.
  • Strong understanding of scalability, reliability, security, observability, and performance engineering.
  • Ability to lead technical strategy while balancing business priorities and organizational goals.
  • Strong communication and stakeholder management skills.

Preferred Qualifications
  • Experience in retail, e-commerce, digital commerce, or customer-facing digital products.
  • Experience supporting:
    • Personalized product search
    • Search ranking and relevance systems
    • Recommendation engines
    • Customer personalization platforms
    • Customer 360 initiatives
  • Experience working with clickstream, behavioral, transactional, and customer identity data.
  • Familiarity with:
    • Recommendation systems
    • Collaborative filtering
    • Embeddings and feature engineering
    • Vector search and semantic search technologies
    • MLOps platforms
    • Feature stores
    • Experimentation frameworks and A/B testing
  • Experience supporting machine learning platforms and production AI/ML workloads.

Education
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent combination of education and professional experience.

Physical Demands & Working Conditions
  • Ability to function in a fast-paced, multi-priority environment.
  • Ability to travel as needed.
  • May require occasional extended hours to support critical business initiatives and production events.

The responsibilities and qualifications outlined above describe the general nature and level of work assigned to this position and are not intended to be an exhaustive list of all duties, responsibilities, or skills required. Duties may be modified at any time based on business needs.
Last revised: 11/01/2024

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